Over-sampling is a proven method for addressing class imbalance [1]}. However, an often cited drawback of over-sampling methods is that they cause a substantial increase in training time for deep learning models because they dramatically increase the number of training batches, which is a natural result of increasing the number of minority class samples [2]}, [3]}. A number of prior attempts to incorporate over-sampling into deep learning have focused on pre-processed minority class augmentations, which generally increase training time [4]}, [5]}, [6]}, [7]}.
